LLM test

What is an LLM test?

An LLM test is typically a repeatable prompt (or prompt set) used to:

  • Measure output quality
  • Catch regressions
  • Test for specific risks (e.g., hallucinations, formatting, refusals)
  • Validate model alignment with task instructions or user expectations

LLM tests can be standalone or part of a broader GenAI testing framework.

Why LLM testing matters

Without structured testing, LLMs may:

  • Produce unpredictable outputs across prompts or contexts
  • Fail to meet quality or tone requirements
  • Regress in performance during updates

Running systematic tests helps ensure LLMs are production-ready.

Types of LLM tests

1. Prompt-based tests

  • Fixed input prompts used to evaluate consistency, accuracy, and safety
  • Track how output changes over time or across versions

2. Scenario-based tests

  • Multi-turn or contextual tests simulating user interactions
  • Helps evaluate agent behavior, memory, or chain-of-thought reasoning

3. Rubric-driven evaluations

  • Tests scored using defined criteria (e.g., clarity, helpfulness, tone)
  • Can be scored manually or with LLM-as-a-Judge

4. Behavioral and stress tests

  • Evaluate how the model handles edge cases, adversarial inputs, or conflicting instructions

5. Regression tests

  • Compare new outputs against baselines from previous versions

Related

Designing a robust LLM test suite is key to continuous evaluation and quality assurance in generative AI applications.

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